Flexural Behaviour of Steel Fibre Reinforced Concrete Tunnel Linings
Bibliographic record
Abstract
The promotion of steel fibre reinforced concrete (SFRC) as a construction material for tunnel linings has prompted a number of researchers to focus on methods of evaluating their flexural strength and stiffness. This thesis presents the results of an experimental and numerical investigation of the flexural behaviour of full-scale steel fibre reinforced concrete tunnel lining segments. A series of a three-point flexure tests were performed to evaluate the maximum load carrying capacity, the load-deformation behaviour and crack propagation characteristics of these segments. The material properties of the steel fibre reinforced concrete were also studied, using both destructive and non-destructive methods. Element compression and tension tests were conducted to characterize the compressive and tensile strength properties of the SFRC. Additionally, computed tomographic scanning was conducted to analyse and estimate the density fraction and fibre orientation of the fibres in SFRC cores. Three-dimensional finite element analyses were conducted to calibrate a concrete damage plasticity constitutive model and provide better understanding of the segment flexural behaviour. The experimental program indicated that the variation in structural performance of the segments was likely due to an inhomogeneity of fibre distribution and orientation. Modifying the numerical model to account for these variations resulted in a more accurate analysis. Furthermore, from the numerical finite element analysis it was found that the non-linear elasto-plastic concrete damage plasticity model in the crack zone of the beam was mesh dependent. Parametric analyses also revealed that the model was particularly sensitive to small changes to the tensile material property input parameters.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".